facility-based planning methodology for rural roads …...4. type of road 5. pavement details 6....
TRANSCRIPT
SHORT COMMUNICATION
Facility-based planning methodology for rural roads using spatialtechniques
Anil Modinpuroju1 • C. S. R. K. Prasad1 • Mukesh Chandra1
Received: 15 May 2016 / Accepted: 15 September 2016 / Published online: 22 September 2016
� Springer International Publishing Switzerland 2016
Abstract Provision of good rural roads to meet the public
facilities changes the characteristics of rural transport. This
paper aims at identifying the roads in a network for
upgradation and maintenance based on the pavement and
develop a methodology for prioritization of the roads using
village facility indices. The study area is located in War-
angal District, Telangana, India. Spatial analysis was car-
ried out in the study area to identify the maximum
coverage distance of the facility to design the village
facility index using ArcGIS software. A priority list of the
road links to upgradation works is prepared by calculating
the link weight using gravity formula. A Geographic
Information System (GIS)-based rural road database is
developed, which will help the authorities in the road
sector to expand the infrastructure facilities for current and
future requirement. Practical applications of the model
exhibit significant advantages over the general practices of
rural road planning in India.
Keywords Rural roads � Facility planning � Geographicinformation system � Spatial analysis
Introduction
Rural Roads play a vital role in the development of any
emergent country. In India, rural roads share more than
85 % of the road network of the country. Hence mainte-
nance that in serviceable condition is essential for the rural
people to get access to public facilities. A well-planned
road network in rural areas is one of the most important
infrastructure elements which improves rural accessibility
and contributes to the rural development as a whole. To
improve the connectivity among villages and also toward
the public facilities, there is an urgent need to improve the
condition of existing roads by planning for upgradation.
Proper planning and development of the rural road net-
work, therefore, assumes its importance in terms of pro-
viding connectivity to the facilities.
In the past, different methodologies were developed for
the rural road network planning. Sufficiency Rating
methods were used in the early 1950s for planning main-
tenance and improvement of US Highways [1]. Makarachi
and Tillotson [2] developed a settlement interaction-based
model for planning in rural areas using gravity formulae.
Ifzal and Ernesto [3] Poverty reduction requires the eco-
nomic growth which is observed from providing good
access to the public facilities like education, market cen-
ters, health services, and others by the accompanied
macroeconomic management and good governance.
Shrestha [4] developed two computer-aided models for
planning and prioritizing district transportation networks
by considering the economic net present value (ENPV), an
economic internal rate of return (EIRR), and the benefit-
cost ratio (B/C ratio). Singh [5] designed an accessibility
index for rural road network planning in developed coun-
tries in GIS environment. Dhamaniya [6] developed a
methodology for maintenance of rural roads by considering
the utility value of habitation and PCI of the road. Shrestha
and Benta [7] considered the parameters like Population
served by links, Person-km, Population served/km, and
Gravity flow model for prioritization of roads. In many
models, the prioritization process is very complex, taking
too many indicators for ranking road links, some of them
& Anil Modinpuroju
1 National Institute of Technology Warangal, Warangal,
Telangana, India
123
Innov. Infrastruct. Solut. (2016) 1:41
DOI 10.1007/s41062-016-0041-8
possibly irrelevant. Kumar and Kumar [8] explained that,
for prioritization of rural roads, generally two broad
approaches are used: (a) sufficiency rating and (b) cost-
benefit analysis. The main drawback of the sufficiency
ratings is its randomness. In the cost-benefit analysis var-
ious costs and benefits associated with a road have to be
evaluated in the same monetary terms, which is a difficult
task. Accessibility to the rural population is considered a
benefit of the investment in rural roads. Thus, the ratio of
population served by a link and its construction cost can be
taken as a good proxy for the expected benefit from a rural
road link. The link serving higher population per unit
investment receives high priority. Typically, rural roads are
constructed to connect the rural settlements, thus improv-
ing accessibility. Therefore, road access to the settlements
may be the most significant indicator.
The papers mainly focused on the development of cri-
teria for the prioritization of links for upgradation in a
network by estimating the link weight using facility indi-
ces. The planning methodology for a model is developed in
different stages in this study. In the first stage, the pave-
ment condition index (PCI) values of the roads were
observed from the field surveys and the roads need
upgradation works to be identified. The facility indices for
habitations were calculated by the spatial analysis and
prioritization of the links for upgradation work is done in
the second stage using gravity formula. The volume of
information related to a rural road is difficult to process,
manage, update, and sort; to obviate these difficulties, it is,
therefore, considered necessary to develop all the spatial
and attribute data in digital format. Therefore, GIS an
essential tool in comprehending the information of spatial
and non-spatial data over a space and time is employed.
Methodology
The proposed methodology can be divided into different
phases. The first phase includes identification of roads for
upgradation and maintenance based on the PCI of a road
and identifying the village and facilities location using GPS
from field observations. The pavement condition index
values are assigned to the roads based on the comfort-
able speed of the vehicle traveled on the road and PCI
values are indexed on a scale from 1 to 5 as shown in
Table 1, where ‘1’ shows worst possible condition and ‘5’
shows the best possible condition of the pavement
(PMGSY Manual). In the present study, comfortable speed
of the vehicle observed from the field surveys by traveling
on each road twice in a year, i.e., before and after the rainy
season in both the direction and car was used as a design
vehicle. The average value of the speed considers as a
normal driving speed of the vehicle.
After collecting the data from the field observation
spatial database was prepared in the Geo-environment
using ArcGIS. In the second stage, the facility index values
of the habitations are estimated from the facility avail-
ability and distance from the facilities to habitation in the
study area by spatial analysis tool using ArcGIS software
and prioritization list is prepared for upgradation work for
the roads based on the link weightage.
Study area and data collection
Dharmasagr Mandal is selected as the study area, which is
located in Warangal district of Telangana state. The con-
nectivity status of the study area is shown in Fig. 1. The
study area has a total road length of 267.10 km and 78 %
connectivity to the habitations. Primary data were collected
from field surveys which are conducted in the study area
and then data which collected included the elements listed
below:
1. Length of each link
2. Average travel time on link
3. Pavement condition of link
4. Type of road
5. Pavement details
6. Location and details of facilities
7. Identification of critical links
8. Missing links in map
9. Population benefited by each link
Handheld GPS instrument was used to collect the
location of facilities and trace out the road in the study
area. Secondary data like toposheet, habitation details as
well as road information data and other ancillary data were
collected from Panchayat Raj department of Warangal
district. Survey of India (SOI) topographic sheet of
1:50,000 scales used to prepare different layers of the study
area.
Table 1 Assessment of PCI based on comfortable normal driving
speed of the vehicle
Normal driving speed
(Km/h)
PCI Description of surface
condition
Over 40 5 Very good
30–40 4 Good
20–30 3 Fair
10–20 2 Poor
Less than 10 1 Very poor
41 Page 2 of 8 Innov. Infrastruct. Solut. (2016) 1:41
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Identification of the roads for upgradation work
To identify the roads to be upgraded field, surveys were
conducted in the study area by traveling along the road in a
design vehicle with a comfortable riding quality and
observed the normal driving speed on the road. PCI values
are assigned to the roads based on the normal driving speed
as explained in Table 1 and the road connected to habita-
tions was observed for upgradation as listed in Table 2 and
the roads which are listed having the PCI value less than or
equal to 2.
Preparation of spatial database
Spatial data store geometric locations of geographic fea-
tures along with attribute information describing what
these features represent. Spatial data are usually stored as
coordinates and topology, which can be mapped. It is often
accessed, manipulated or analyzed through GIS. Spatial
information is necessary to specify the shape and location
of geographic features as well as spatial relationships
between such features. The spatial relationships implicit on
a map determine what the map conveys to the reader. For
Fig. 1 Connectivity status of the study area
Table 2 Roads connected to habitations for upgradation work
S. no. From habitation To habitation
1 Velair Gundlasagar
2 Velair Banglapally (Katkur Ramaiahpalle)
3 Kammaripet Chinthalathanda
4 Chinthalathanda Bhilyanaik thanda
5 Kammaripet thanda Mallikudurla
6 Dharmasagar Kammaripet thanda
7 Dharmasagar Karunapuram
8 Dharmasagar Shodasapally
9 Waddera colony Thatikayala
10 Elukurthi Mallakkapally
11 Upparapally Saipeta
Innov. Infrastruct. Solut. (2016) 1:41 Page 3 of 8 41
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example, tasks such as finding the route between points can
be easily accomplished. GIS programs also allow one to
complete these tasks.
To prepare the spatial database, preliminary, SOI map of
scale 1: 50,000 for the study area collected from Panchayat
Raj Department of Warangal District. The map was scan-
ned and entered into the GIS environment for registration
purpose. After Geo-referencing, different features of the
study area were digitized as different layers. These layers
were created in Arc Catalog environment according to the
feature type; point layer for the location of habitations,
rural infrastructures, etc., polygon for a Mandal boundary,
and line for road and other linear features. The road which
was traced out from the field using GPS instrument was
imported into the map and digitized as a line feature. The
shape files were created for various layers with the same
coordinate system. The scanned toposheet was defined to
this coordinate system before Geo-referencing is done.
Once the required shape files of different layers are created,
digitization of features is done. Digitization is the process
of converting the geographic features on an analog map
into digital format. In this process, the x, y coordinates of
these features are automatically recorded and stored as
spatial data. The spatial data were created in Arc Map
environment and the total spatial database organization
involved the process of identifying the content of spatial
data and the actual process of creating the database in Arc
Catalog.
Preparation of non-spatial database
Non-spatial data are tabular or textual data describing the
geographic characteristics of features. This type of data has
no specific location in space. It can, however, have a geo-
graphic component and be linked to a geographic location.
The attribute data can be stored in Excel or Access
spreadsheets and joined to their corresponding spatial fea-
tures using GIS capability. The data were entered into the
database for all features collected via field survey and dig-
itized from toposheet. The database contains all fields for
which the data were collected from the facility, availability
of the block which is in the study area, information collected
about the roads inventories, pavement details, habitations,
rural infrastructures, etc., the database was developed and
Fig. 2 Accessibility levels from the high school facility
41 Page 4 of 8 Innov. Infrastruct. Solut. (2016) 1:41
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stored on Arc Catalog package. This database shows the
real-time attributes to analyze the features.
Spatial and non-spatial database are integrated to form
Geo-database. The geo-database is the common data stor-
age and management framework for ArcGIS. It combines
‘‘geo’’ (spatial data) with ‘‘database’’ (data repository) to
create a central data repository for spatial data storage and
management.
Estimation of village facility index (VFI)
Facility index of a village refers to the level of infras-
tructure availability from the habitations. The facilities
considered for the analysis is given below:
1. Education facilities: it includes a primary school,
middle school, high school, an intermediate college,
degree college.
2. Medical facilities: it includes sub-centers, maternity,
child welfare centers, primary health centers, and
hospitals.
3. Economic activity centers: it includes markets, petrol
bunks, retail shops, cold storages.
4. Transport and communication facilities: it includes bus
stand, railway station, post office, banks, electrical
substations, etc.
For each major facility, most popular and widely
accepted approach is to find cumulative weight by
assigning weights to each parameter under that major
facility.
Garg [9], if Ii is the index of particular facility ‘‘f’’ of ith
habitation, then the facility index is estimated using Eq. 1.
Ii ¼X
j
n
1
� �WjXj; ð1Þ
where Wj: weight of jth sub facility: wj 9 dj
wj ¼Total no: of villages in block
Villages having jth sub facility
Xj: value or availability of jth sub facility in ith habi-
tation; n: number of sub facility available in ith habitation;
dj: distance factor, values are considering as 1, 0.5, 0.0 if
these facilities are available only at a distance of less than
0.5 km, in between 0.5 km and maximum coverage dis-
tance of the facility, more than maximum coverage dis-
tance of the facility, respectively.
The Village Facility Index (VFI) value is obtained by
cumulating all the major facility indices shown in Eq. 2.
VFI ¼ EFIþMFIþ ECOFIþ TFIþ CFI, ð2Þ
where
0%
20%
40%
60%
80%
100%
120%
0 0.5 1 1.5 2 2.5 3 3.5 4Distance (km)
cove
rage
(%)
Fig. 3 Effect of coverage in service distance from primary school
0%
20%
40%
60%
80%
100%
120%
0 1 2 3 4 5 6 7 8
cove
rage
(%)
Distance (km)
Fig. 4 Effect of coverage in service distance from high school
Table 3 Coverage distances for different facilities
S. no. Type of facility MCDF (km)
1 Education facilities
Primary school 2
Middle school 3
High school 4
ITI/junior college/degree college 5
2 Health facilities
ANM centers 3
PHC 5
Veterinary hospital 4
Community health Center 5
3 Economic activity centers
Retail shops 1
Cold storage 5
Market 5
4 Transportation and communication centers
Railway station 5
Bus stand 1
Post office 3
Banks 5
Petrol outlet 5
Electric substation 11kv 5
Innov. Infrastruct. Solut. (2016) 1:41 Page 5 of 8 41
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EFI: educational facility index; MFI: medical facility
index; ECOFFI: economic activity facility index; TFI:
transportation facility index; CFI: communication facility
index.
Identifying the maximum coverage distance
from the facility (MCDF)
Maximum coverage distance is the distance at which the
facility covers more habitations. In this study, the MCDF
values are identified based on the availability of the
infrastructure and at what desired distance the facility
covers the maximum habituations. Fixing a maximum
coverage service distance is a strategic decision. The
maximum coverage distance is fixed in the ranges from 0.5
to 5 km. The value corresponds to the average human
walking speed which is 5 km/h [7]. Relaxing the service
distance allows covering much more habitations, but the
difficulty to achieve facility rises accordingly, which is not
desirable. To identify the MCDF buffer,spatial analysis
was carried out for each facility in the Warangal district
using ArcGIS software. For example, Fig. 2 shows the
accessibility levels of the habitation from the high school.
From this analysis, numbers of habitations covered from a
facility at different distance levels are noticed.
Figure 3 shows the covering percentage of the habita-
tions at different service distance from a primary school.
The figure shows that more than 50 % of the habitations
are covered in less than 0.5 km and then increasing with
the lower increment of 14, 10, 8, and 5 % in the distant
levels of less than 1, 1.5, 2, and 2.5 km, respectively.
The coverage distance fixed 2 km for the primary school
at which more than 80 % habitations are covered. Simi-
larly, the effect of service distance for the high school
facility is shown in Fig. 4.
From the Fig. 4, the coverage percentage rapidly
increases up to 4 km with nearly 80 % and then increase
with lower increments. The coverage distance identified
from the analysis for high school is 4 km.
Similarly, the analysis is carried to all the facilities and
coverage distances are identified as shown in Table 3.
The MCDF values are considered for calculation of VFI
of habitations and the results obtained using Eqs. 1 and 2
are shown in Table 4.
Table 4 Village facility indices
of the study areaName of the habitation EFI MFI TFI ECOFI CFI VFI
Velair 9.87 41.42 1.83 80 1.15 134.27
Upparapally 4.935 20.71 0.915 40 1.075 67.635
Banglapally (Katkur Ramaiahpalle) 5.565 20.71 0.915 40 1.075 68.265
Gollakistampally 5.565 20.71 0.915 40 1.075 68.265
Kammaripet 5.565 20.71 1.83 40 1.075 69.18
Chinthalathanda 5.565 20.71 0.915 40 1.075 68.265
Kammaripet thanda 4.935 20.71 0.915 40 1.075 67.635
Gundlasagar 7.22 7.82 0.915 1 1.075 18.03
Bhilyanaik thanda 6.59 7.82 0.915 1 1.075 17.4
Shodasapally 5.565 7.82 1.83 1 1.15 17.365
Mallikudurla 9.87 15.42 1.83 1 1.075 29.195
Kummarigudem 5.565 7.82 1.83 1 1 17.215
Narayanagiri 9.87 4.82 1.83 1 1 18.52
Devnoor 9.87 4.82 1.83 1 1 18.52
Dharmasagar 115.87 41.42 1.83 27 1.15 187.27
Waddera colony 57.565 20.82 0.915 14 1.075 94.375
Elukurthi 59.22 23.23 1.83 14 1.075 99.355
Narsingaraopally 5.565 2.41 0.915 1 1 10.89
Ramannagudem 5.565 2.41 0.915 1 1 10.89
Saipeta 9.87 2.41 1.83 1 1 16.11
Thatikayala 9.87 10.12 1.83 1 1.15 23.97
Peddapendyala 9.87 15.42 1.83 1 1.15 29.27
Karunapuram 5.565 7.71 1.83 1 1.075 17.18
Mallakkapally 9.87 15.42 1.83 1 1.075 29.195
Gunturpally 4.935 7.71 0.915 1 1.075 15.635
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Facility-based model for prioritization of road
The existing rural road planning models in India were
developed based on the interaction between the settlements
and prioritization of the roads. In the present study,
upgradation of the network was done by strengthening the
road links by providing the new overlay. Prioritization for
upgradation of road link is based on the population bene-
fited by the link, facility index of the villages, and the
Pavement Condition Index (PCI) of the road. Facility index
values are obtained from the facility availability from the
village. Further, the link weight is estimated using the
Eq. 3 given below.
Wij ¼ Pi � Pj� �
Fi � Fj� �
=d2; ð3Þ
where,
Wij: Weightage; Pi and Pj: population of villages i and j;
Fi and Fj: facility index values of village i and j; d: shortest
distance between village i and j; if Fi - Fj = 0 then take
this value as 1
The link weight and PCI value of the links are taken as a
measure for strengthening/upgradation of the pavement
based on the criteria which are given below.
– First strengthening of the through rout based on which
link had higher weight and who’s PCI value is\or =2.
– After this strengthening of those link routes based on
which link had higher weight and who’s PCI value is\or =2.
The result obtained and the priority list of the roads for
upgradation work is given in Table 5.
Conclusions
A facility-based model is developed to improve the road
condition which is given, due to the consideration, by
selecting the road for upgradation based on the link weight
and PCI value. Spatial analysis of the facility in the study
area helps the organization to understand the influence of the
facility of maximum habitations covered from the facility
and useful for facility allocation problems. From the spatial
analysis of the existing infrastructure in the study area, it is
observed that facilities like primary school, Middle school,
High school, ANM center, and Post office cover the maxi-
mum habitations with in the desirable distance. It shows the
accessible levels of these facilities are good in the study area.
There is a need to identify the suitable location for providing
the public facilities like PHC, cold storage, community
health centers, and banks. Rural road organizations can use
this model effectively for planning and management of the
roads. By upgrading the roads, the travel time on the road can
be reduced and the interaction between the habitations can be
improved. Even though the model was developed for Indian
conditions, it can be practiced in any developing country for
planning rural road networks.
References
1. Highway Research Board (1952). Highway sufficiency ratings.
HRB Bulletin 228, Washington, DC
2. Makarachi AK, Tillotson HT (1991) Road planning in rural areas
of developing countries. Eur J Oper Res 53:279–287
Table 5 Priority list of the roads for upgradation work
S. no. From habitation To habitation Weightage (Wij) Priority of the road
1 Velair Gundlasagar 9.83 9
2 Velair Banglapally (Katkur Ramaiahpalle) 48.32 3
3 Kammaripet Chinthalathanda 0.35 11
4 Chinthalathanda Bhilyanaik thanda 24.08 5
5 Kammaripet thanda Mallikudurla 10.14 8
6 Dharmasagar Kammaripet thanda 470 1
7 Dharmasagar Karunapuram 23.27 6
8 Dharmasagar Shodasapally 11.92 7
9 Waddera colony Thatikayala 9.20 10
10 Elukurthi Mallakkapally 91.94 2
11 Upparapally Saipeta 28.72 4
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3. Ifzal Ali, Ernesto Pernia M (2003) Infrastructure and poverty
reduction: what is the connection?. Asian Development Bank,
Manila
4. Shrestha CB (2003) Developing a computer-aided methodology
for district road network planning and prioritization in Nepal.
Trans Res Board 3:157–174
5. Singh AK (2010) GIS based rural road network planning for
developing countries. J Transp Eng 1943–5436:0000212
6. Dhamaniya A (2013) Methodology of maintenance criteria of
PMGSY roads in second phase of planning—a case study. J Indian
Soc Remote Sens. doi:10.1007/s12524-013-0338-4
7. Shrestha J, Benta A (2013) Multi-objective analysis of cost and
social benefits in rural road networks. Int J Civ Environ Struct
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8. Kumar A, Kumar P (1999) User-friendly model for planning rural
roads. J Trans Res Board. doi:10.3141/1652-05
9. Garg (2008) spatial planning of infrastructural facilities in rural
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